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Imke Loyack
BentoML simplifies the process of building machine learning services. It offers a standard, Python-based architecture for deploying and maintaining production grade APIs. This architecture allows users to easily package trained models using any ML framework for online and offline model serving.
With its ability to address different machine learning workflows, it grants you full control over model management operations. It also acts as an alternative to serving models with the SageMaker tool, and a model deployment platform on top of AWS services like Elastic Kubernetes Service (EKS), Lambda, or Fargate.
Cortex: An open source platform for deploying machine learning models as production web servic
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KFServing provides a Kubernetes Custom Resource Definition (CRD) for serving machine learning models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.
As enterprises grow their investments in data platforms, they increasingly want to go beyond using data for internal analytics and start integrating predictions from machine learning (ML) models to create a competitive advantage for their products and services. For example, financial institutions deploy ML models to detect fraudulent transactions in real-time, and retailers use ML models to personalize product recommendations for each customer.
These mission-critical applications require an MLOps platform that can scale to process millions of predictions per second at low latency and with high availability while providing visibility into how models are performing in production. This becomes even more of a challenge with compute-intensive deep learning models that power natural language processing and computer vision applications.
To accelerate model serving and MLOps on Databricks, we are excited to announce that Cortex Labs, a Bay Area-based MLOps startup, has joined Databricks. Cortex Labs is the maker of Cortex, a popular open-source platform for deploying, managing, and scaling ML models in production. Cortex Labs was backed by leading infrastructure software investors Pitango Venture Capital, Engineering Capital, Uncorrelated Ventures, at.inc/, and Abstraction Capital, as well as angels Jeremey Schneider and Lior Gavish.
The Twitter ML Platform encompasses the ML tools and services Cortex provides to accomplish our mission. The ML Platform provides tools that span the full ML spectrum, from dataset preparation, to experimentation, to deploying models to production. The subject of this blog post is only one of the components of this platform: internally designated as DeepBird. This framework is for training and productionising deep learning models. Implemented using Python, TensorFlow (v2), Lua Torch (v1). The framework has undergone various changes since the summer of 2017, and we wanted to share our experience here.
TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms and experiments while allowing developers to keep the same server architecture and APIs. TensorFlow Serving provides seamless integration with TensorFlow models, and can also be easily extended to other models and data.
NVIDIA Triton Inference Server simplifies the deployment of AI models at scale in production. The open-source serving software allows the deployment of trained AI models from any framework, such as TensorFlow, NVIDIA, PyTorch or ONNX, from local storage or cloud platform. It supports an HTTP/REST and GRPC protocol, allowing remote clients to request interfacing for any model managed by the server.
Multi Model Server is an open-source tool for serving deep learning and neural net models for inference, exported from MXNet or ONNX. The easy-to-use and flexible tool utilises REST-based APIs to handle state prediction requests. Multi Model Server uses java 8 or a later version to serve HTTP requests.
Bootstrap ML workflows on the cloud with minimal effort and complete reliability. Deplofai is built for developers who work on data science and machine learning projects, and want a better developer experience. At Deploifai, we enable integrations to tools that developers use the most, such as MLFlow, DVC, and cloud services such as AWS S3 and more, so developers don't have to make any additional efforts for setting up and using the best tools that the industry has to provide. Why use Deploifai: - Flexible and Cloud agnostic: Base your project's infrastructure on any of the popular cloud services. - Simple: Create a project, connect to your repo on GitHub, and start running training/experiments on the cloud. - Efficient: Collaborate with your team, and work on the same projects and share data and compute resources. - Effective: Work with the best of tools in the ML industry to enable developers to generate better results from their work.
InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions.
MLRun is an end-to-end open-source MLOps orchestration framework to manage and automate your entire analytics and machine learning lifecycle, from data ingestion, through model development to full pipeline deployment. MLRun eases the development of machine learning pipelines at scale and helps ML teams build a robust process for moving from the research phase to fully operational production deployments.
PrimeHub, an open-source pluggable MLOps platform on the top of Kubernetes for teams of data scientists and administrators. PrimeHub equips enterprises with consistent yet flexible tools to develop, train, and deploy ML models at scale. By improving the iterative process of data science, data teams can collaborate closely and innovate fast.
The Iguazio MLOps Platform accelerates and scales development, deployment and management of your AI applications with MLOps and end-to-end automation of machine learning pipelines. The platform includes an online and offline feature store, fully integrated with automated model monitoring and drift detection, model serving and dynamic scaling capabilities, all packaged in an open and managed platform.
Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more.
A common grumble among data science or machine learning researchers or practitioners is that putting a model in production is difficult. As a result, some claim that a large percentage, 87%, of models never see the light of the day in production.
Feature stores are emerging pivotal components in the modern machine learning development cycle. As more data scientists and engineers work together to successfully put models in production, having a singular store to persist cleaned and featurized data is becoming an increasing necessity as part of the model development cycle shown.
Request Batching: Not all models in production are employed for real-time serving. Often, models are scored in large batches of requests. For example, for deep learning models, parallelizing these image requests to multiple cores, taking advantage of hardware accelerators, to expedite batch scoring and utilize hardware resources is worthy of consideration.
Each consideration has its merits. Each consideration has either an open source solution addressing each problem or a managed solution from a vendor. Evaluate how each best fits and meets all the considerations into your existing machine learning tooling stack.
TensorFlow Serving is a flexible, high-performance serving system for machine learning models designed for production environments. Created by Google, it is one of the first serving tools ever to exist. Flexible, high-performance serving system for machine learning models, designed for production environments. It supports both HTTP and gRPC APIs for both inference and management. It can serve multiple models or multiple versions of the same model simultaneously, which could be beneficial for new versions and A/B testing experimental models. Unlike TorchServe, it can serve models without Python handlers.
Machine learning deployment is a crucial step in bringing the benefits of data science to real-world applications. With the increasing demand for machine learning deployment, various tools and platforms have emerged to help data scientists and developers deploy their models quickly and efficiently.
Additionally, there are fewer dependencies on external data sources and cloud services, and the local processing power is often adequate for computing algorithmically complex models. Moreover, it is relatively simple to debug an offline model when failures occur, or to tune hyperparameters since it runs on powerful servers. The deployment of machine learning models in diverse settings is an essential aspect of machine learning deployment.
By simplifying the development and deployment of machine learning workflows, Kubeflow ensures that models are traceable while offering a comprehensive suite of powerful machine learning tools and architectural frameworks to perform various machine learning tasks efficiently. The platform also includes a multifunctional UI dashboard, making it easy to manage and track experiments, tasks, and deployment runs.
Gradio is an open-source, flexible user interface (UI) that is compatible with both Tensorflow and Pytorch models. It is freely available, making it accessible to anyone who wishes to use it. By leveraging the open-source Gradio Python library, developers can quickly and easily create user-friendly, adaptable UI components for their machine learning models, APIs, or any other functions using just a few lines of code.
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